package snu.bi.MSAgent.ActRecog;

import libsvm.*;
import java.io.*;
import java.util.*;

import android.util.Log;

class svm_predict {
	private static double atof(String s)
	{
		return Double.valueOf(s).doubleValue();
	}

	private static int atoi(String s)
	{
		return Integer.parseInt(s);
	}

	private static double predict(String input, DataOutputStream output, svm_model model, int predict_probability) throws IOException
	{
		int correct = 0;
		int total = 0;
		double error = 0;
		double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
		Log.i("svm_test", "SVM_START");
		int svm_type=svm.svm_get_svm_type(model);
		int nr_class=svm.svm_get_nr_class(model);
		double[] prob_estimates=null;

		if(predict_probability == 1)
		{
			if(svm_type == svm_parameter.EPSILON_SVR ||
			   svm_type == svm_parameter.NU_SVR)
			{
				System.out.print("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma="+svm.svm_get_svr_probability(model)+"\n");
			}
			else
			{
				int[] labels=new int[nr_class];
				svm.svm_get_labels(model,labels);
				prob_estimates = new double[nr_class];
				output.writeBytes("labels");
				for(int j=0;j<nr_class;j++)
					output.writeBytes(" "+labels[j]);
				output.writeBytes("\n");
			}
		}
		//////////////////////
		String line = input;
		
		StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");

		double target = atof(st.nextToken());
		int m = st.countTokens()/2;
		svm_node[] x = new svm_node[m];
		for(int j=0;j<m;j++)
		{
			x[j] = new svm_node();
			x[j].index = atoi(st.nextToken());
			x[j].value = atof(st.nextToken());
		}

		double v;
		if (predict_probability==1 && (svm_type==svm_parameter.C_SVC || svm_type==svm_parameter.NU_SVC))
		{
			v = svm.svm_predict_probability(model,x,prob_estimates);
			output.writeBytes(v+" ");
			for(int j=0;j<nr_class;j++)
				output.writeBytes(prob_estimates[j]+" ");
			output.writeBytes("\n");
		}
		else
		{
			v = svm.svm_predict(model,x);
			//output.writeBytes(v+"\n"); TODO: HERE!!
			
		}

		if(v == target)
			++correct;
		error += (v-target)*(v-target);
		sumv += v;
		sumy += target;
		sumvv += v*v;
		sumyy += target*target;
		sumvy += v*target;
		++total;
		//////////////////////
		if(svm_type == svm_parameter.EPSILON_SVR ||
		   svm_type == svm_parameter.NU_SVR)
		{
			System.out.print("Mean squared error = "+error/total+" (regression)\n");
			System.out.print("Squared correlation coefficient = "+
				 ((total*sumvy-sumv*sumy)*(total*sumvy-sumv*sumy))/
				 ((total*sumvv-sumv*sumv)*(total*sumyy-sumy*sumy))+
				 " (regression)\n");
		}
		else
			System.out.print("Accuracy = "+(double)correct/total*100+
				 "% ("+correct+"/"+total+") (classification)\n");
		Log.i("svm_test", "SVM_END");
		return v;
	}

	private static void exit_with_help()
	{
		System.err.print("usage: svm_predict [options] test_file model_file output_file\n"
		+"options:\n"
		+"-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n");
		System.exit(1);
	}

	public static double run(String argv[], svm_model model) throws IOException
	{
		int i, predict_probability=0;
		double ret = 0;
/*		// parse options
		Log.v("svm_test", argv[0]);
		for(i=0;i<argv.length;i++)
		{
			if(argv[i].charAt(0) != '-') break;
			++i;
			switch(argv[i-1].charAt(1))
			{
				case 'b':
					predict_probability = atoi(argv[i]);
					break;
				default:
					System.err.print("Unknown option: " + argv[i-1] + "\n");
					exit_with_help();
					
			}
		}
		if(i>=argv.length-2){
			exit_with_help();
			
		}
		Log.i("svm_test", "THIS?");*/
		try  
		{
//			BufferedReader input = new BufferedReader(new FileReader(argv[i]));
			String input = argv[0];
			DataOutputStream output = new DataOutputStream(new BufferedOutputStream(new FileOutputStream(argv[0+2])));
//			svm_model model = svm.svm_load_model(argv[0+1]);
/*			if(predict_probability == 1)
			{
				if(svm.svm_check_probability_model(model)==0)
				{
					System.err.print("Model does not support probabiliy estimates\n");
					System.exit(1);
				}
			}
			else
			{
				if(svm.svm_check_probability_model(model)!=0)
				{
					System.out.print("Model supports probability estimates, but disabled in prediction.\n");
				}
			}*/
			Log.i("svm_test", "Starting>>>");
			ret = predict(input,output,model,predict_probability);
			Log.i("svm_test", "END>>>");
//			input.close();
			output.close();
		} 
		catch(FileNotFoundException e) 
		{
			exit_with_help();
			
		}
		catch(ArrayIndexOutOfBoundsException e) 
		{
			exit_with_help();
		}
		return ret;
	}
}
